RNA_Content_Analysis_Chemostat_12032015
nathanbrandt
2016-12-21
Experiment overview
Write a detailed description of your experiment here including the goal of the analysis and your interpretation of the results.
If you still see this text it means that you have not described the experiment and whatever follows is meaningless. ###############################
This code is designed for use with the Accuri flow cytometer, which is equiped with the following lasers and filters
- Blue laser (488 nm)
- FL1 filter = 514/20nm GFP
FL3 filter = 575/25nm YFP
- Yellow/green laser (552 nm)
- FL2 filter = 610/20nm mCherry, dtomato
FL4 filter = 586/15nm DsRed
Requirements
In order to run this code you need:
+ to predefine your gates using the gating.R script
+ the gates.Rdata workspace, which contains the gates used in this script
+ the path of the directory(ies), given the variable names dir1, dir2… that contain .fcs files named A01.fcs, A02.fcs, A03.fcs…
+ a tab delimited sample sheet in each directory with the following rows:
+ the variable names are changed in chunk 2 named “Variable Names”
Output
This script generates a summary of results followed by quality control plots.
1 Step 1: Load relevant libraries
# This is a function that just makes sure you have a package, or installs it for you without prompting
requireInstall <- function(packageName,isBioconductor=F) {
if ( !try(require(packageName,character.only=T)) ) {
print(paste0("You don't have ",packageName," accessible, ",
"I'm gonna install it"))
if (isBioconductor) {
source("http://bioconductor.org/biocLite.R")
biocLite(packageName)
} else {
install.packages("packageName", repos = "http://cran.us.r-project.org")
}
}
return(1)
}
#Load libraries
requireInstall("flowCore",isBioconductor=T)## Loading required package: flowCore## [1] 1requireInstall("flowViz",isBioconductor=T)## Loading required package: flowViz## Loading required package: lattice## [1] 1requireInstall("flowStats")## Loading required package: flowStats## Loading required package: fda## Loading required package: splines## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:flowCore':
##
## %&%##
## Attaching package: 'fda'## The following object is masked from 'package:graphics':
##
## matplot## Loading required package: cluster## Loading required package: flowWorkspace## Loading required package: ncdfFlow## Loading required package: RcppArmadillo## Loading required package: BH## Loading required package: gridExtra## [1] 1requireInstall("Hmisc")## Loading required package: Hmisc## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'Hmisc'## The following object is masked from 'package:gridExtra':
##
## combine## The following objects are masked from 'package:base':
##
## format.pval, round.POSIXt, trunc.POSIXt, units## [1] 1requireInstall("reshape2")## Loading required package: reshape2## [1] 1requireInstall("ggplot2")## [1] 1requireInstall("flowWorkspace")## [1] 1requireInstall("ggcyto", isBioconductor=T)## Loading required package: ggcyto## [1] 1requireInstall("gridExtra")## [1] 12 Step 2: Read in .fcs files, an Rdata file containing the gates sample sheet(s) that contains four columns with
- column1 = Well
- column2 = Strain
- column3 = Staining
- column4 = Media
- column5 = Userdefined
#Read in all data for analysis. Data should be in individual directories that contain .fcs files and a corresponding sample sheet with a generic format. FCS file names should be unaltered e.g AO1.fcs, A02.fcs, ...H12.fcs
#An abitrary number of directories can be used named dir1, dir2, dir3...with a corresponding flowData.1, flowData.2, flowData.3...and sample.sheet.1, sample.sheet.2, sample.sheet.3...
#load the Rdata file containing the gates
#load("gates.Rdata")
#Define the directory, or directories, containing your .fcs files using absolute path names
dir1 <- "/Users/nathanbrandt/Google Drive/Transfer_Folder/RNA_Content_Analysis_SPARQ/TotalRNA_FY4_Chemostat_12032015"
#Read in all the fcs files in the directory, with alter.names changing "-" to "."
flowData <- read.flowSet(path = dir1, pattern=".fcs", alter.names=TRUE)
#Read in the sample sheet that should be in each directory that contains the .fcs files.
sample.sheet <- read.csv(paste(path="/Users/nathanbrandt/Google Drive/Transfer_Folder/RNA_Content_Analysis_SPARQ/", "TotalRNA_FY4_Chemostat_SampleSheet_12032015.csv", sep=""))
#Change names of samples to those specified in the sample sheets
sampleNames(flowData) <- paste(sample.sheet[,5], sample.sheet[,1], sep=" ")#Check how many cells were counted in each fcs file
fsApply(flowData, each_col, length)[1:6]## [1] 100000 100000 100000 100000 100000 100000total <- fsApply(flowData, each_col, length)[1:6] #total counts per sample
#Print the medians of data values for each measurement
fsApply(flowData, each_col, median)## FSC.A SSC.A FL1.A FL2.A FL3.A FL4.A FSC.H
## control A01 734391.5 86183.0 3028.0 322 921.0 290 1139156.5
## Clim_0.12 A02 729134.0 90393.5 15715.0 940 3734.0 795 1130571.5
## Clim_0.2 A03 1122036.0 138938.0 40430.0 2335 9904.0 1951 1545426.5
## Clim_0.3 A04 940498.0 120600.0 43241.0 2506 11014.5 2101 1320793.0
## Nlim_0.12 A05 785781.0 92392.5 15459.0 816 3419.0 719 1172048.5
## Nlim_0.2 B01 704853.0 85645.5 19682.0 1090 4496.0 944 1073701.0
## Nlim_0.3 B02 472980.5 71961.0 18599.0 1122 4654.0 937 742028.5
## Clim_0.12 B03 371753.0 49228.0 12098.0 668 2821.0 578 624264.5
## Clim_0.2 B04 500216.0 70642.0 18991.5 1049 4838.0 905 806946.0
## Clim_0.3 B05 611290.5 82616.0 27130.0 1799 7662.0 1559 943873.0
## control C01 683963.5 73198.0 3025.0 322 897.0 291 1093173.0
## Clim_0.12 C02 692707.5 88932.0 15934.0 929 3731.0 797 1086519.5
## Clim_0.2 C03 868536.0 133002.5 35161.0 1916 8530.0 1579 1190596.5
## Clim_0.3 C04 1029944.0 116237.0 40935.0 2320 9676.0 2007 1459579.5
## Nlim_0.12 C05 837993.0 101483.0 15583.0 754 3342.0 696 1237961.0
## Nlim_0.2 D01 603652.0 91829.0 18021.0 968 4278.0 834 915401.5
## Nlim_0.3 D02 511576.0 76441.5 18294.5 1163 4770.0 988 807096.0
## Clim_0.12 D03 524918.0 64583.5 12372.0 686 2901.0 616 842041.0
## Clim_0.2 D04 517485.5 63968.0 20232.0 1149 4976.0 1002 836032.0
## Clim_0.3 D05 619619.0 75890.0 26322.0 1738 6850.0 1532 943443.0
## SSC.H FL1.H FL2.H FL3.H FL4.H Width Time
## control A01 121896.5 3431.0 230 1679 455 61 181
## Clim_0.12 A02 127111.5 15977.0 830 3817 830 61 181
## Clim_0.2 A03 179506.0 35800.0 1918 8581 1725 71 282
## Clim_0.3 A04 156233.5 36888.5 1992 9192 1802 69 405
## Nlim_0.12 A05 125254.0 14873.0 737 3517 805 63 167
## Nlim_0.2 B01 118108.0 18634.0 989 4427 989 60 162
## Nlim_0.3 B02 99848.5 17595.0 945 4406 930 55 181
## Clim_0.12 B03 73764.5 12832.0 610 3099 671 50 163
## Clim_0.2 B04 104369.5 19651.0 978 4987 950 54 172
## Clim_0.3 B05 112392.5 24732.0 1500 6795 1422 59 278
## control C01 105276.5 3456.0 228 1675 453 59 176
## Clim_0.12 C02 129588.5 16483.0 838 3866 837 60 179
## Clim_0.2 C03 174238.0 31169.0 1569 7414 1402 68 285
## Clim_0.3 C04 145524.5 34673.0 1831 8065 1713 69 412
## Nlim_0.12 C05 133316.0 15004.5 694 3494 797 63 176
## Nlim_0.2 D01 124472.0 17642.0 887 4326 895 59 163
## Nlim_0.3 D02 106789.0 17701.0 1007 4614 986 56 184
## Clim_0.12 D03 92631.0 12972.0 614 3169 700 55 169
## Clim_0.2 D04 91767.0 20245.0 1043 4956 1018 55 174
## Clim_0.3 D05 99499.5 22996.0 1383 5882 1356 60 273samples.num <- length(flowData) #number of samples3 Step 3: apply filters to data and generate plots showing the effect on filtering
##Subset the data by applying sequential gates##
#apply doublet gate
flowData.singlets <- Subset(flowData, pg.singlets)
fsApply(flowData.singlets, each_col, length)[1:samples.num]
singlets <- fsApply(flowData.singlets, each_col, length)[1:samples.num]
barplot(singlets/total, ylim=c(0,1), ylab = "Proportion singlet cells", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#apply debris gate
filteredData <- Subset(flowData.singlets, pg.nondebris)
fsApply(filteredData, each_col, length)[1:samples.num]
non.debris <- fsApply(filteredData, each_col, length)[1:samples.num]
barplot(non.debris/total, ylim=c(0,1), ylab = "Proportion singlet and nondebris cells", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#########
#filteredData is the variable name for the data filtered of doublets and debris that are used for all subsequent analyses
##########
#this gate defines nongfp cells
gfp.neg <- Subset(filteredData, pg.nongfp)
fsApply(gfp.neg, each_col, length)[1:samples.num]
non.gfp <- fsApply(gfp.neg, each_col, length)[1:samples.num]
barplot(non.gfp/non.debris, ylim=c(0,1), ylab = "Proportion cells with no GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#this gate defines gfp cells
gfp.pos <- Subset(filteredData, pg.gfp)
fsApply(gfp.pos, each_col, length)[1:samples.num]
gfp.cells <- fsApply(gfp.pos, each_col, length)[1:samples.num]
barplot(gfp.cells/non.debris, ylim=c(0,1), ylab = "Proportion cells with GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#this gate defines high GFP cells
gfp.hi <- Subset(filteredData, pg.hi.gfp)
fsApply(gfp.hi, each_col, length)[1:samples.num]
hi.gfp.cells <- fsApply(gfp.hi, each_col, length)[1:samples.num]
barplot(hi.gfp.cells/non.debris, ylim=c(0,1), ylab = "Proportion cells with high GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))filteredData <- flowData4 Step 4: Data analysis
4.1 diagnostic values can be defined for plotting purposes
#define critical values that can superimposed on plots for easy visual comparison
gfp.bg <- 3.9 #a background value for GFP
gfp.wt <- 5.9 #a value for wildtype GFP expression
red.bg <- 3.03 #a background value for the red channel
red.wt <- 3.75 #a value for wildtype Red expression
haploid.fsc <- 6e5 #an empirical value for forward scatter for haploids
diploid.fsc <- 7e5 #an empirical value for forward scatter for diploids
gfp.norm <- 0.935 #an empricial value for gfp expression normalized by forward scatter
red.norm <- 0.57 #an empricial value for red expression normalized by forward scatter
gfp.red.norm <- 1.5 #an empricial value for gfp expression normalized by red channel4.2 Extract data from fcs files to generate statistics and boxplots
#record summary statistics for each sample in a matrix named summary.stats
summary.stats <- matrix(data = NA, nrow = length(filteredData), ncol = 18, dimnames = list(sampleNames(filteredData),c("FSC_median","FSC_mean", "FSC_sd","FL1_median", "FL1_mean","FL1_sd","normalizedGFP_median", "normalizedGFP_mean", "normalizedGFP_sd","FL2_median","FL2_mean","FL2_sd","normalizedRed_median","normalizedRed_mean", "normalizedRed_sd","GFPnormalizedByRed_median", "GFPnormalizedByRed_mean","GFPnormalizedByRed_sd")))
#use the sample containing the minimum number of points after filtering for doublets and debris to define the number of data points retained for all samples
sample.size <- min(fsApply(filteredData, each_col, length))
print(sample.size)## [1] 100000comparison.FSC <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL2 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1NormFsc <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL2NormFsc <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1NormFL2 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
#for each sample plot a histogram of the normalized data, raw FSC and raw GFP per row
par(mfrow=c(1,2), mar=c(5.1,2.1,2.1,2.1), oma=c(1.5,2,1,1))
#extract data from flowFrames to plot histograms of values and record summary statistics
for (i in 1:length(filteredData)){
temp <- exprs(filteredData[[i]]) #exprs() extracts a matrix of the values from the flowframe
##########################################
#record summary statistics for the sample#
##########################################
#FSC
summary.stats[i,1] <- median(temp[,1])
summary.stats[i,2] <-mean(temp[,1])
summary.stats[i,3] <- sd(temp[,1])
#FL1
summary.stats[i,4] <- median(temp[,3])
summary.stats[i,5] <-mean(temp[,3])
summary.stats[i,6] <- sd(temp[,3])
#FL1 (GFP) divided by FSC
summary.stats[i,7] <- median(temp[,3]/temp[,1])
summary.stats[i,8] <-mean(temp[,3]/temp[,1])
summary.stats[i,9] <- sd(temp[,3]/temp[,1])
#FL2
summary.stats[i,10] <- median(temp[,4])
summary.stats[i,11] <-mean(temp[,4])
summary.stats[i,12] <- sd(temp[,4])
#FL2 (Red) divided by FSC
summary.stats[i,13] <- median(temp[,4]/temp[,1])
summary.stats[i,14] <-mean(temp[,4]/temp[,1])
summary.stats[i,15] <- sd(temp[,4]/temp[,1])
#FL1 (GFP) divided by FL2 (Red)
summary.stats[i,16] <- median(temp[,3]/temp[,4])
summary.stats[i,17] <-mean(temp[,3]/temp[,4])
summary.stats[i,18] <- sd(temp[,3]/temp[,4])
##############################################
#plot histograms of the channels of interest##
##############################################
###############
#Green channel#
###############
#FL1 (GFP)
hist(log10(temp[,3]), br=1000, xlab = "log10(FL1)", main = "FL1")
abline(v=gfp.bg, col="yellow", lty=2, lwd=2)
abline(v=gfp.wt, col="green", lty=2, lwd=2)
legend("topleft", legend=paste("median FL1 = ",round(median(temp[,3]), digits=4),sep=""))
#GFP divided by FSC
hist(temp[,3]/temp[,1], br=500, xlab = "FL1/FSC", main = "FL1/FSC")
abline(v=gfp.norm, col="green", lty=2, lwd=2 )
legend("topleft", legend=paste("median GFP / FSC=",round(median(temp[,3]/temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
###############
#Red channel#
###############
#FL2 (Red)
hist(log10(temp[,4]), br=500, xlab = "log10(FL2)", main = "FL2")
abline(v=red.bg, col="yellow", lty=2, lwd=2)
abline(v=red.wt, col="red", lty=2, lwd=2)
legend("topleft", legend=paste("median FL2=",round(median(temp[,4]), digits=4),sep=""))
#FL2 divided by FSC
hist(temp[,4]/temp[,1], br=500, xlab = "FL2/FSC", main = "FL2/FSC")
abline(v=red.norm, col="red", lty=2, lwd=2 )
legend("topleft", legend=paste("median FL2 / FSC=",round(median(temp[,4]/temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
###############
#Other#########
###############
#FL1 divided by FL2
hist(temp[,4]/temp[,3], br=500, xlab = "FL2/FL1", main = "FL1/FL2")
abline(v=gfp.red.norm, col="purple", lty=2, lwd=2)
legend("topleft", legend=paste("median FL1 / FL2=",round(median(temp[,4]/temp[,3]), digits=4),sep=""))
#FSC
hist(log10(temp[,1]), br=500, xlab = "log10(FSC)", main = "FSC", xlim=c(4,8))
abline(v=haploid.fsc, col="blue", lty=2, lwd=2)
abline(v=diploid.fsc, col="grey", lty=2, lwd=2)
legend("topleft", legend=paste("median FSC=",round(median(temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
print("-------------------------------------------------------")
print("-----------------------------------")
print("----------------------")
############################################################
#keep the data set for generating boxplots comparing values#
############################################################
#Note that the amount of data kept for each sample is defined by the lowest count among all the samples.
comparison.FSC[1:sample.size,i] <- temp[1:sample.size,1] #FSC
comparison.FL1[1:sample.size,i] <- temp[1:sample.size,3] #FL1 (GFP)
comparison.FL1NormFsc[1:sample.size,i] <- temp[1:sample.size,3]/temp[1:sample.size,1] #GFP/FSC
comparison.FL2[1:sample.size,i] <- temp[1:sample.size,4] #FL2
comparison.FL2NormFsc[1:sample.size,i] <- temp[1:sample.size,4]/temp[1:sample.size,1] #FL2/FSC
comparison.FL1NormFL2[1:sample.size,i] <- temp[1:sample.size,3]/temp[1:sample.size,4] #FL1/FL2
}
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## [1] "----------------------"par(mfrow=c(1,1)) #change number of plots per row back to standard4.3 Overview of data distributions
par(mar=c(8.1,4.1,4.1,2.1)) #create more space at lower margin
boxplot(comparison.FSC, names=sampleNames(filteredData), notch = TRUE, col = "gray", ylab="FSC", cex.axis=0.5,las=2, outline=F)
abline(h=haploid.fsc, lty=2, col=2)
abline(h=diploid.fsc, lty=2, col=3)
boxplot(comparison.FL1, names=sampleNames(filteredData), notch = TRUE, col = "lightgreen", ylab="FL1", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.bg ,lty=2, lwd=3, col="yellow")
abline(h=gfp.wt, lty = 2, lwd=3, col="green")
boxplot(comparison.FL1NormFsc, names=sampleNames(filteredData), notch = TRUE, col = "green", ylab="FL1/FSC", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.norm, lty=2, lwd=3, col="blue")
boxplot(comparison.FL2, names=sampleNames(filteredData), notch = TRUE, col = "pink", ylab="FL2", cex.axis=0.5,las=2, outline=F)
abline(h=red.bg, lty=2, lwd=3, col="pink")
abline(h=red.wt, lty=2, lwd=3, col="red")
boxplot(comparison.FL2NormFsc, names=sampleNames(filteredData), notch = TRUE, col = "red", ylab="FL2/FSC", cex.axis=0.5,las=2, outline=F)
abline(h=red.norm, lty=2, lwd=3, col="red")
boxplot(comparison.FL1NormFL2, names=sampleNames(filteredData), notch = TRUE, col = "purple", ylab="FL1/FL2", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.red.norm, lty=2, lwd=3, col="purple")
par(mar=c(5.1,4.1,4.1,2.1)) #reset margins to default
#generate a summary table containing all the recorded statistics
print(summary.stats)## FSC_median FSC_mean FSC_sd FL1_median FL1_mean
## control A01 734391.5 805865.6 375294.6 3028.0 3405.727
## Clim_0.12 A02 729134.0 799293.1 366834.9 15715.0 86863.651
## Clim_0.2 A03 1122036.0 1176833.9 456889.4 40430.0 182278.228
## Clim_0.3 A04 940498.0 1010104.7 407907.6 43241.0 202883.918
## Nlim_0.12 A05 785781.0 1028305.3 756683.7 15459.0 122791.069
## Nlim_0.2 B01 704853.0 917633.5 635773.1 19682.0 72527.680
## Nlim_0.3 B02 472980.5 543664.1 304611.3 18599.0 51664.112
## Clim_0.12 B03 371753.0 444839.4 297369.7 12098.0 50885.794
## Clim_0.2 B04 500216.0 553112.6 264698.3 18991.5 33672.997
## Clim_0.3 B05 611290.5 656223.0 246077.5 27130.0 62286.890
## control C01 683963.5 756080.1 366074.3 3025.0 3397.974
## Clim_0.12 C02 692707.5 764255.4 358084.8 15934.0 84327.486
## Clim_0.2 C03 868536.0 927386.1 389518.9 35161.0 171357.942
## Clim_0.3 C04 1029944.0 1108043.5 424067.6 40935.0 178904.744
## Nlim_0.12 C05 837993.0 1097751.4 758504.0 15583.0 128347.254
## Nlim_0.2 D01 603652.0 779220.1 534085.3 18021.0 64279.562
## Nlim_0.3 D02 511576.0 597397.2 295541.2 18294.5 53360.794
## Clim_0.12 D03 524918.0 595797.5 324969.8 12372.0 48161.613
## Clim_0.2 D04 517485.5 568501.9 265351.3 20232.0 36134.546
## Clim_0.3 D05 619619.0 664216.8 250123.4 26322.0 60155.358
## FL1_sd normalizedGFP_median normalizedGFP_mean
## control A01 2302.098 0.004097719 0.004190421
## Clim_0.12 A02 351438.351 0.022541959 0.133297739
## Clim_0.2 A03 567875.124 0.036087745 0.230174285
## Clim_0.3 A04 589703.024 0.044345356 0.288414854
## Nlim_0.12 A05 398446.679 0.019880647 0.128374992
## Nlim_0.2 B01 262233.042 0.027480917 0.086464952
## Nlim_0.3 B02 223310.715 0.039286023 0.115019302
## Clim_0.12 B03 202720.541 0.033073961 0.141777414
## Clim_0.2 B04 136111.229 0.038043898 0.065420988
## Clim_0.3 B05 283899.775 0.042980250 0.099948954
## control C01 2314.595 0.004371919 0.004492780
## Clim_0.12 C02 335605.721 0.024116614 0.136916971
## Clim_0.2 C03 527856.336 0.040567718 0.295662176
## Clim_0.3 C04 542414.339 0.038576538 0.226581616
## Nlim_0.12 C05 420980.556 0.018530154 0.120954001
## Nlim_0.2 D01 238441.571 0.029701831 0.087002385
## Nlim_0.3 D02 229940.896 0.034358874 0.100084547
## Clim_0.12 D03 188932.260 0.024285326 0.090119459
## Clim_0.2 D04 150403.698 0.038773373 0.067583546
## Clim_0.3 D05 271846.042 0.041482640 0.095032618
## normalizedGFP_sd FL2_median FL2_mean FL2_sd
## control A01 0.002148754 322 350.3830 271.4712
## Clim_0.12 A02 0.561352072 940 4326.6729 17724.2874
## Clim_0.2 A03 0.780153282 2335 10296.3011 36209.0358
## Clim_0.3 A04 0.900324939 2506 11075.7522 34182.2279
## Nlim_0.12 A05 0.480848320 816 5677.0564 18590.5461
## Nlim_0.2 B01 0.388298804 1090 3555.5192 12737.7787
## Nlim_0.3 B02 0.565137267 1122 2815.1628 15279.4307
## Clim_0.12 B03 0.605286790 668 2489.4794 15487.6554
## Clim_0.2 B04 0.271672081 1049 1869.3290 11431.7036
## Clim_0.3 B05 0.436638432 1799 4154.8950 26656.1855
## control C01 0.002302972 322 348.3642 265.9441
## Clim_0.12 C02 0.565843890 929 4226.1651 19433.4320
## Clim_0.2 C03 1.004951470 1916 9477.3916 39539.5036
## Clim_0.3 C04 0.737635174 2320 9577.0267 31365.3324
## Nlim_0.12 C05 0.444540207 754 5874.0092 19544.4030
## Nlim_0.2 D01 0.381853966 968 3104.5453 12112.9054
## Nlim_0.3 D02 0.469117137 1163 3057.2257 20647.1369
## Clim_0.12 D03 0.350216010 686 2364.9900 13129.5534
## Clim_0.2 D04 0.277169958 1149 2076.4191 17133.7737
## Clim_0.3 D05 0.414180522 1738 4057.0472 29203.5931
## normalizedRed_median normalizedRed_mean normalizedRed_sd
## control A01 0.0004284430 0.0004719936 0.0003803247
## Clim_0.12 A02 0.0012896294 0.0070213467 0.0435829213
## Clim_0.2 A03 0.0021024314 0.0136106323 0.0685506046
## Clim_0.3 A04 0.0026019139 0.0166703699 0.0787378971
## Nlim_0.12 A05 0.0010285991 0.0061536709 0.0352396637
## Nlim_0.2 B01 0.0015344030 0.0044334856 0.0312989067
## Nlim_0.3 B02 0.0023453488 0.0067635137 0.0596158635
## Clim_0.12 B03 0.0017821591 0.0071641556 0.0485338051
## Clim_0.2 B04 0.0020998689 0.0038630386 0.0310763032
## Clim_0.3 B05 0.0028915328 0.0071715632 0.0541647664
## control C01 0.0004577279 0.0005052016 0.0003984882
## Clim_0.12 C02 0.0013412099 0.0070029583 0.0363699255
## Clim_0.2 C03 0.0022176469 0.0189257860 0.1151340853
## Clim_0.3 C04 0.0022151274 0.0129234804 0.0701222620
## Nlim_0.12 C05 0.0008838760 0.0055679333 0.0208841746
## Nlim_0.2 D01 0.0015962806 0.0043514934 0.0287983864
## Nlim_0.3 D02 0.0021900443 0.0059996713 0.0509582840
## Clim_0.12 D03 0.0013073564 0.0046833494 0.0326977670
## Clim_0.2 D04 0.0022207130 0.0040673452 0.0307104161
## Clim_0.3 D05 0.0027727086 0.0069577958 0.0582342361
## GFPnormalizedByRed_median GFPnormalizedByRed_mean
## control A01 NA NaN
## Clim_0.12 A02 17.69162 Inf
## Clim_0.2 A03 NA NaN
## Clim_0.3 A04 NA NaN
## Nlim_0.12 A05 NA NaN
## Nlim_0.2 B01 NA NaN
## Nlim_0.3 B02 17.03895 Inf
## Clim_0.12 B03 NA NaN
## Clim_0.2 B04 NA NaN
## Clim_0.3 B05 NA NaN
## control C01 NA NaN
## Clim_0.12 C02 NA NaN
## Clim_0.2 C03 NA NaN
## Clim_0.3 C04 17.98909 Inf
## Nlim_0.12 C05 NA NaN
## Nlim_0.2 D01 NA NaN
## Nlim_0.3 D02 16.22702 Inf
## Clim_0.12 D03 NA NaN
## Clim_0.2 D04 17.72493 Inf
## Clim_0.3 D05 NA NaN
## GFPnormalizedByRed_sd
## control A01 NA
## Clim_0.12 A02 NaN
## Clim_0.2 A03 NA
## Clim_0.3 A04 NA
## Nlim_0.12 A05 NA
## Nlim_0.2 B01 NA
## Nlim_0.3 B02 NaN
## Clim_0.12 B03 NA
## Clim_0.2 B04 NA
## Clim_0.3 B05 NA
## control C01 NA
## Clim_0.12 C02 NA
## Clim_0.2 C03 NA
## Clim_0.3 C04 NaN
## Nlim_0.12 C05 NA
## Nlim_0.2 D01 NA
## Nlim_0.3 D02 NaN
## Clim_0.12 D03 NA
## Clim_0.2 D04 NaN
## Clim_0.3 D05 NAsummary.stats <- as.data.frame(summary.stats)4.4 Quantitation of relative FL1 signal
baseline.FL1 <- summary.stats$FL1_median[1]
barplot(summary.stats$FL1_median/baseline.FL1, ylab="Relative FL1 median expression", las=2, cex.names = 0.5, names.arg=sampleNames(filteredData))
4.5 Quantitation of forward scatter
baseline.FSC <- summary.stats$FSC_median[1]
barplot(summary.stats$FSC_median/baseline.FSC, ylab="Relative median FSC", las=2, cex.names = 0.5, names.arg=sampleNames(filteredData))
4.6 Population composition
pop.composition <- rbind(non.gfp/non.debris,gfp.cells/non.debris,hi.gfp.cells/non.debris)
barplot(pop.composition, ylab="Proportion of population", legend=c("No GFP", "Normal GFP", "High GFP"),las=2, cex.names = 0.5,names.arg=sampleNames(filteredData))5 Step 5: Quality control
5.1 Gates
###First flowset
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData.1, xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - singlets gate")
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData.1, displayFilter=TRUE, xlim=c(0,3e6), ylim=c(0,3e5), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - nondebris gate")
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.1, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - non GFP gate")
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.1, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - GFP gate")
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData.1, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - high GFP gate")
################
###Second flowset
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData.2, xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - singlets gate")
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData.2, displayFilter=TRUE, xlim=c(0,3e6), ylim=c(0,3e5), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - nondebris gate")
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.2, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - non GFP gate")
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.2, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - GFP gate")
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData.2, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - high GFP gate")
#####Attempted as loop below to plot each one individually and is not working
for (i in 1:length(filteredData)){
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData[i], xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData[i], displayFilter=TRUE, xlim=c(0,3e5), ylim=c(0,3e6), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData[i], displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData[i], displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData[i], xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
}5.2 Data transformation for visualization
#In order to look at QC plots the data is transformed using the logicle transform, which is a log transform for high values that transitions to a linear transformation near zero values
#This is simply for visualization purposes
lgcl <- logicleTransform(w = 0.5, t= 10000, m=4.5) #the parameters w,t, and m define the transformation parameters
#Dataset 1 tranformation applied to every channel except width and time
dataLGCLTransform <- transform(filteredData,'FSC.A' = lgcl(`FSC.A`), 'SSC.A' =lgcl(`SSC.A`), 'FL1.A' = lgcl(`FL1.A`), 'FL2.A' = lgcl(`FL2.A`), 'FL3.A' = lgcl(`FL3.A`), 'FL4.A' = lgcl(`FL4.A`),'FSC.H' = lgcl(`FSC.H`),'SSC.H' = lgcl(`SSC.H`),'FL1.H' = lgcl(`FL1.H`),'FL2.H' = lgcl(`FL2.H`),'FL3.H' = lgcl(`FL3.H`),'FL4.H' = lgcl(`FL4.H`)) 5.3 Effect of time
#The effect of time on signal (of which there shouldn't be any)
i <- 1
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
i <- 2
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
i <- 3
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
}5.4 Plots of FL1 versus FSC
i <- 1
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 2
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 3
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(2,6), sampleNames(filteredData)[i])
}5.5 Plots of FSC versus SSC
i <- 1
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 2
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 3
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])
}